xAI-CV: An Overview of Explainable Artificial Intelligence in Computer Vision

• URL: http://arxiv.org/abs/2509.18913v1
• Date: Tue, 23 Sep 2025 12:33:54 GMT
• Title: xAI-CV: An Overview of Explainable Artificial Intelligence in Computer Vision
• Authors: Nguyen Van Tu, Pham Nguyen Hai Long, Vo Hoai Viet,
• Abstract summary: This paper surveys four representative approaches in xAI for visual perception tasks.<n>We analyze their underlying mechanisms, strengths and limitations, as well as evaluation metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has become the de facto standard and dominant paradigm in image analysis tasks, achieving state-of-the-art performance. However, this approach often results in "black-box" models, whose decision-making processes are difficult to interpret, raising concerns about reliability in critical applications. To address this challenge and provide human a method to understand how AI model process and make decision, the field of xAI has emerged. This paper surveys four representative approaches in xAI for visual perception tasks: (i) Saliency Maps, (ii) Concept Bottleneck Models (CBM), (iii) Prototype-based methods, and (iv) Hybrid approaches. We analyze their underlying mechanisms, strengths and limitations, as well as evaluation metrics, thereby providing a comprehensive overview to guide future research and applications.

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